An intelligent game agent is one that acquires knowledge about the world and then acts on that knowledge

From AI: a Modern Approach: creation of computer programs that emulate four things:

Thinking humanity

Thinking rationally (Sheer Logic)

Acting humanity (Turing test)

Acting rationally

What is “Game AI”:

AI is the creation of computer programs that emulate acting and thinking like a human, as well acting and thinking rationally

Game Ai is the code in a game that makes the computer-controlled opponents (or cooperative elements) appear to make smart decisions when the game has multiple choices for a given simulation results in behaviors that are relevant, effective, and useful

We are only interested in the responses that the system will generate and don’t care about how the system arrived it

We care about how system acts not how in how it thinks

In old days AI programming was called gameplay programming

How Game AI Evolve:

Page 34 Game AI Timeline

Patterns Approach

At the start the patterns were fixed

Then, they used simple random number generators

After that, used complex number generators

PI-Approach:

Allowing computer to cheat; in other words have more information about world so the decisions it takes seems remarkably smart!

Examples:

Gathering resources

Giving gifts to computer (unlimited recourses, no time constraint…)

Rubber banding:

If you are ending a race and you are beating AI-Controlled cars by too much, some games simply speed up the other cars until they’ve caught up with you, and then they return to normal

In the past CPU was concerned with graphics more than AI, now many VEGA Cards have their own PU so CPU is free for AI calculations

1-2% of CPU time was dedicated for AI-Calculations not 10-35% of CPU time in consumed in AI-Processing

What Game AI is NOT:

Game AI is considered as: collision avoidance (path-finding), player controls, UI and game animation!

This book emphasize on the differentiation between:

Game AI makes decision where there are multiple options or directions to play

Making a decision from pool of solutions/animations/paths are more “Find the BEST” solution for particular input

The main AI might have many equally good solutions but needs to consider planning resources, player attributes and so on to decisions for game’s bigger picture

Difference between low-level AI and high-level AI (soda example)

Gamers will not care about your shiny new algorithm if it doesn’t feel smarter and fun!

Game AI is not the best code; its best use of code and a large dollop of “whatever works (WW)”

There isn’t elegant solution for everything

How this definition differs from Academic AI:

Academic AI has 2 goals:

Understand intelligent entities, which will in turn help us to understand ourselves

Game AI focus on acting as human with less dependence of total rationality

Game AI needs to model the highs and lows of human task performance instead of searching for best decision at all time (This is for entertainment reason for sure!)

Example: Chess Game:

How it will be developed as Academic AI

How it will be developed as Game AI

The people who coded Big Blue didn’t care if Kasparov was having fun when playing against it. But people behind Chessmaster games surely spend a lot of time thinking about fun factor, especially the default difficulty settings

Applicable mind science and psychology theory:

This section give you ideas and notions of how to break down intelligence tasks in same way human mind does it

Also these three were called: reptilian brain, mammalian brain, human brain

These brain regions operate independently by using local working memory areas accessing neighboring synaptic connections to do specific tasks for organism. But these regions are also interconnected so to perform global level tasking

Knowledge Base and Learning:

The information is stored in the form of small changes in brain nerve at the synapse connection level

If you use a particular neural pathway it gets stronger!

Games AI may use principle of plasticity that’s instead of creating a set-in-stone list of AI behaviors and reactions to human actions, we keep the behavior exhibited by AI malleable

See the human response

AI systems would require a dependable system for determining what’s “good” to learn whereas the human brain just stores everything

Punching example

Rate of memory reinforcement and degradation in human brain is not the same for all systems (i.e. pain aversion is may never fully extinguish) so that lead to long term memory. This is a good example of nature dynamic hard coding

Differences between short-term & long-term memory

Hitting in arm example (page 42) à as short term & long term. Observing results

Brian make uses of modulators, chemicals that are released into the blood that:

Enhance firing of neurons in specific brain areas

Leads to more focused set-mind

Flavoring memories of particular contextual way

Modulators could be applied in Game AI using State-Based AI (i.e. alert/angry state)

Hurt guard using a state system with modifiers, could stay in Normal Guard State with “aggressive” modulator

When you decide that your game is going to use learning techniques, carefully decide gow you want the game to come up with its learned data (keeping statistics that seem to work against human is one way)

Question # 3

Influence maps makes the AI opponent seems smart from one or two applications: (44)

RTS Game save pathfinding algorithm

Sport games (scoring goals and block enemy passes)

Influence maps advantages

Have low iterations because the information to store is so specific and also the storing of misleading information is also minimized

Cognition:

Some questions:

How does the brain know which info to deal with first

Which pieces to throw away

Brain does this by systems that quickly categorize and prioritize incoming data

Cognition can be thought of as taking all your incoming sense data, called perceptions, and filtering them through your innate knowledge (instinctual and intuitive) as well as your reasoning centers (stored memories) to come up with understanding of what those perceptions mean to you

Be carful not to oversimplify that makes your-Controller output predictable

Sound ranges (if you make a big noise outside sound range what should happened?)

Also take into consideration environment attributes (closed/open area)

Most AI systems are just different ways of searching through variety of possibilities

The topography (State-Space of game) of your game’s possibilities can be used to conceptually consider the best AI technique to use:

If your game’s possible outcomes to different perceptions is mostly isolated islands of response with no real gray conditions state-based system in suitable for you

If the range of possible responses is more continuous and a graph out more like a rolling hillside neural networks based system. Because they work better at identifying local maxima and minima in continuous fields of response

Theory of Mind (ToM):

That will help us because we want to create systems that seems intelligent

ToM means that open person has the ability to understand others as having minds and a worldview that are separate from his own

ToM technical definition: knowing that others are intentional agents, and interpret their minds through theoretical concepts of intentional states such as beliefs and desires

False-Belief-Task Test (46)

If he passes the test that means he can model others facts, desires and believes

Turing test (that’s exactly what we want in our games!)

We must model mind not behavior

Rules in combat game (53)

The player will create a ToM about them and think they are working together!

Bounded Optimality:

Main goal of game AI is to emulate human performance level not perfect rationality

One of the reasons that humans make mistakes is the idea of bounded optimality (BO)

BO = System will make its best decision given computation (and other resources) restrictions

Decision making by people is limited under some factors:

Quality and depth of relevant knowledge

Cognitive speed

Problem solving ability

We create optimal programs rather than optimal actions!

Lessons from Robotics:

Simplicity of design and solution

Instead of trying to navigate areas by recognizing obstacles and trying …. Insectile creations that blindly use general purpose methods to force their way over obstacles

Lesson here is what while others spend years trying tech-heavy methods for cleave ring getting around obstacles and failing, Brooks’s robot design are being incorporated into robots that are headed to Mars!

Theory of Mind:

Trying to give the robot ability to show desires and intents instead of raw behaviors